add 最大边际相关性的MaxMarginalRelevanceExampleSelector

prompts_relevance_example_selector.ipynb

@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.example_selector import MaxMarginalRelevanceExampleSelector\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
+    "\n",
+    "example_prompt = PromptTemplate(\n",
+    "    input_variables=[\"input\", \"output\"],\n",
+    "    template=\"Input: {input}\\nOutput: {output}\",\n",
+    ")\n",
+    "\n",
+    "# These are a lot of examples of a pretend task of creating antonyms.\n",
+    "examples = [\n",
+    "    {\"input\": \"高兴\", \"output\": \"悲伤\"},\n",
+    "    {\"input\": \"个子高\", \"output\": \"个子矮\"},\n",
+    "    {\"input\": \"精力充沛\", \"output\": \"昏昏欲睡\"},\n",
+    "    {\"input\": \"晴朗\", \"output\": \"阴暗的阴暗的\"},\n",
+    "    {\"input\": \"多风\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"经济下滑\", \"output\": \"业绩增长\"},\n",
+    "    {\"input\": \"飞翔\", \"output\": \"天空\"},\n",
+    "    {\"input\": \"教育\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"小孩儿\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"开心\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"消防员\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"程序员\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"理财师\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"学生\", \"output\": \"平静的\"},\n",
+    "    {\"input\": \"计算机\", \"output\": \"平静的\"},\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_selector = MaxMarginalRelevanceExampleSelector.from_examples(\n",
+    "    # This is the list of examples available to select from.\n",
+    "    examples, \n",
+    "    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
+    "    OpenAIEmbeddings(), \n",
+    "    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
+    "    FAISS, \n",
+    "    # This is the number of examples to produce.\n",
+    "    k=2\n",
+    ")\n",
+    "mmr_prompt = FewShotPromptTemplate(\n",
+    "    # We provide an ExampleSelector instead of examples.\n",
+    "    example_selector=example_selector,\n",
+    "    example_prompt=example_prompt,\n",
+    "    prefix=\"Give the antonym of every input\",\n",
+    "    suffix=\"Input: {adjective}\\nOutput:\", \n",
+    "    input_variables=[\"adjective\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Give the antonym of every input\n",
+      "\n",
+      "Input: 理财师\n",
+      "Output: 平静的\n",
+      "\n",
+      "Input: 经济下滑\n",
+      "Output: 业绩增长\n",
+      "\n",
+      "Input: 投资\n",
+      "Output:\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Input is a feeling, so should select the happy/sad example as the first one\n",
+    "print(mmr_prompt.format(adjective=\"投资\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}